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Engineering and Maintenance Planning… with Bad EAM Data?

Asset-intensive organizations rely heavily on their Engineering and Maintenance Planning teams to help facilitate large project roll-outs. These groups must capture, organize, and manage vast amounts of maintenance, engineering, and design data for the assets involved in the projects. They capture data for their assets, such as operating specifications, EAM attributes, engineering documents, recommended spare parts lists, parts catalogs, maintenance guides, recommended maintenance procedures, and more. Ensuring that the data meets the requirements of the organization is a huge task that can be virtually unmanageable without tools to simplify and aid the process.

Capital Projects and Information Handover

A capital build project for an asset-intensive business typically requires a log time to complete, with many groups contributing data, including vendors, suppliers, and maintenance planners. Data comes in many formats: from books, files, spreadsheets, and databases. Asset-intensive organizations may input data for hundreds of thousands of assets from spreadsheets into an EAM/CMMS system such as IBM Maximo, SAP PM, Oracle eAM, Infor EAM and others as part of a capital build. CMMS data management of these spreadsheets manually can result in duplication of data, data being overwritten or omitted, and so on. Yet, it is this asset and maintenance data upon which corporate maintenance strategies, maintenance personnel planning, and spare parts inventories are based. If the data is not accurate or complete, neither are any of the resulting plans and strategies.

Incomplete and inaccurate EAM and CMMS data results in:

An inability to correctly staff a project with maintenance technicians

An inability to optimize spare parts inventories

An inability to estimate the actual time to complete the “data build” project

An inability to plan for full production as early as possible

An inability to trust the reliability, asset performance, or safety guards in place during early operations

New EAM Implementations

A new EAM implementation experiences the same issues with incomplete and inaccurate data as a capital build project. Organizations may attempt to move their existing data into a new EAM system, yet fail to sanitize the data as part of the process. At the same time, they may be loading data for new assets. Without the ability to visualize the data, they may create duplicate assets, overwrite assets, and compromise other important business goals in the process. They also find it very difficult to assess the completeness of their data.

Incomplete and inaccurate EAM and CMMS data results in:

Poor performance of the new EAM system

Sub-optimal maintenance performance

Increased time required to complete the implementation

An inability to correctly update staffing

An inability to optimize spare parts inventories

An inability to ensure assets can be operated safely and effectively

An inability to estimate the actual time to complete the project

Operational Changes to Assets

Assets in an asset-intensive organization are seldom static. New assets are added, assets are decommissioned and retired, or moved to a new location. Assets are upgraded, engineering changes are made, and parts are replaced.

Asset modification is complex, requiring a high degree of coordination between various activities, including project design, cost estimates, procurement, project fulfillment, and documentation. The need to continue to support ongoing production capacity while changing, all the while maintaining safety and cost control, can be extremely challenging. Information produced from many sources must be available, at the same time as data is changing.

Incomplete and inaccurate EAM and CMMS data results in:

Even greater degradation of the data over time

Major cost overruns

Unplanned production outages

A high level of frustration for project team members

Resources Available to Help

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Greg Dee, President at HubHead Corp., sits down with members of VIZIYA to discuss practical solutions to tackle your maintenance master data quality issues. The three big culprits: asset hierarchy, task lists, and work order types.